Abstract

This paper develops an analytical model for probabilistic area coverage in terms of the target detectionprobability. A decision fusion framework is utilized to infer the presence or absence of the target.Analytical results are derived for the target detection and false alarm probabilities in the presence ofcorrelated sensor noise. The spatially correlated sensor observations are utilized to select a subset ofsensors to meet the specified area coverage. Two new sensor selection schemes are proposed formaximizing information theoretic measures such as joint entropy. The sensor selection schemes areanalyzed extensively based on simulations. The results show that the proposed sensor selection schemeoutperforms two state-of-the-art sensor selection schemes: constrained random sensor selection anddisjoint random sensor selection.

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